IEEE

Deep Graph Convolutional Autoencoder With Conditional Normalizing Flow for Power Distribution Systems Fault Classification and Location

Abstract: Accurate fault classification and location are critical to ensure the reliability and resilience of large-scale power distribution systems (PDSs). The existing data-driven works in this area ...
Frontiers

RS-STGCN: Regional-Synergy Spatio-Temporal Graph Convolutional Network for emotion recognition

Decoding emotional states from electroencephalography (EEG) signals is a fundamental goal in affective neuroscience. This endeavor requires accurately modeling the complex spatio-temporal dynamics of ...
GitHub

American Sign Language (ASL) Alphabet Recognition

This project aims to create a deep learning model from scratch using PyTorch to be able to classify images of the American Sign Language (ASL) alphabet. The goal is for it to be able to accurately and ...
IEEE

Conv4Rec: A 1-by-1 Convolutional Autoencoder for User Profiling Through Joint Analysis of Implicit and Explicit Feedback

Abstract: We introduce a new convolutional autoencoder architecture for user modeling and recommendation tasks with several improvements over the state of the art. First, our model has the flexibility ...
GitHub

PyTorch Essential Training: Working with Images

This is the repository for the LinkedIn Learning course PyTorch Essential Training: Working with Images. The full course is available from LinkedIn Learning. Machine learning developers and data ...